Overview

Dataset statistics

Number of variables13
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.9 KiB
Average record size in memory104.2 B

Variable types

Numeric13

Alerts

mean radius is highly overall correlated with radius errorHigh correlation
mean smoothness is highly overall correlated with mean compactness and 2 other fieldsHigh correlation
mean compactness is highly overall correlated with mean smoothness and 4 other fieldsHigh correlation
mean symmetry is highly overall correlated with mean smoothness and 2 other fieldsHigh correlation
mean fractal dimension is highly overall correlated with mean smoothnessHigh correlation
radius error is highly overall correlated with mean radius and 2 other fieldsHigh correlation
compactness error is highly overall correlated with mean compactness and 1 other fieldsHigh correlation
concave points error is highly overall correlated with mean compactness and 2 other fieldsHigh correlation
worst symmetry is highly overall correlated with mean symmetryHigh correlation

Reproduction

Analysis started2023-05-03 20:12:16.017327
Analysis finished2023-05-03 20:13:03.874397
Duration47.86 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

mean radius
Real number (ℝ)

Distinct443
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62177262
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:04.033237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30558295
Q10.49263675
median0.6084351
Q30.74620183
95-th percentile0.95353866
Maximum1
Range1
Interquartile range (IQR)0.25356508

Descriptive statistics

Standard deviation0.19549617
Coefficient of variation (CV)0.31441746
Kurtosis-0.34161511
Mean0.62177262
Median Absolute Deviation (MAD)0.12340182
Skewness0.0085739591
Sum353.78862
Variance0.038218751
MonotonicityNot monotonic
2023-05-03T20:13:04.325892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
2.5%
0.539393423 4
 
0.7%
0.7295259235 3
 
0.5%
0.6383097949 3
 
0.5%
0.5771455311 3
 
0.5%
0.3742145532 3
 
0.5%
0.4933924889 3
 
0.5%
0.4424628915 3
 
0.5%
0.5955764849 3
 
0.5%
0.5690879155 3
 
0.5%
Other values (433) 527
92.6%
ValueCountFrequency (%)
0 1
0.2%
0.09807822056 1
0.2%
0.1029960638 1
0.2%
0.1069849718 1
0.2%
0.1610001175 1
0.2%
0.1637462928 1
0.2%
0.2045779551 1
0.2%
0.2075080142 1
0.2%
0.2076204808 1
0.2%
0.2098662779 1
0.2%
ValueCountFrequency (%)
1 14
2.5%
0.9949203509 1
 
0.2%
0.9935581501 1
 
0.2%
0.9901384538 1
 
0.2%
0.9884209504 1
 
0.2%
0.9818473296 1
 
0.2%
0.9744935678 1
 
0.2%
0.9723752044 1
 
0.2%
0.9720213893 1
 
0.2%
0.9666880833 1
 
0.2%

mean texture
Real number (ℝ)

Distinct473
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56237594
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:04.614537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.24485076
Q10.42574036
median0.56046197
Q30.69231872
95-th percentile0.89670917
Maximum1
Range1
Interquartile range (IQR)0.26657836

Descriptive statistics

Standard deviation0.19447451
Coefficient of variation (CV)0.34580873
Kurtosis-0.29243226
Mean0.56237594
Median Absolute Deviation (MAD)0.13269543
Skewness-0.0035675075
Sum319.99191
Variance0.037820335
MonotonicityNot monotonic
2023-05-03T20:13:04.903964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
1.2%
0.3567466875 3
 
0.5%
0.6372572028 3
 
0.5%
0.5306717663 3
 
0.5%
0.4929778313 3
 
0.5%
0.4001269766 3
 
0.5%
0.4617194631 3
 
0.5%
0.5633012681 3
 
0.5%
0.4611992129 3
 
0.5%
0.6063756634 3
 
0.5%
Other values (463) 535
94.0%
ValueCountFrequency (%)
0 1
0.2%
0.05368150862 1
0.2%
0.07982432821 1
0.2%
0.08738160326 1
0.2%
0.09263708054 1
0.2%
0.09413346547 1
0.2%
0.0963737552 1
0.2%
0.1214117901 1
0.2%
0.1578125698 1
0.2%
0.1647953342 1
0.2%
ValueCountFrequency (%)
1 7
1.2%
0.9911889953 1
 
0.2%
0.9860314911 1
 
0.2%
0.9736891792 1
 
0.2%
0.971728248 1
 
0.2%
0.970419102 1
 
0.2%
0.9691084649 1
 
0.2%
0.9569137101 1
 
0.2%
0.9519327637 1
 
0.2%
0.9344959439 1
 
0.2%

mean smoothness
Real number (ℝ)

Distinct470
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56589336
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:05.180148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28038799
Q10.44334625
median0.56920411
Q30.68605991
95-th percentile0.84141341
Maximum1
Range1
Interquartile range (IQR)0.24271366

Descriptive statistics

Standard deviation0.17467595
Coefficient of variation (CV)0.30867292
Kurtosis-0.16923299
Mean0.56589336
Median Absolute Deviation (MAD)0.12160709
Skewness-0.0026690856
Sum321.99332
Variance0.030511689
MonotonicityNot monotonic
2023-05-03T20:13:05.496897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
0.9%
0.6299808369 5
 
0.9%
0.7122967582 4
 
0.7%
0.687260486 4
 
0.7%
0.7991058834 4
 
0.7%
0.6812499178 3
 
0.5%
0.6509033443 3
 
0.5%
0.7205687557 3
 
0.5%
0.7405085705 3
 
0.5%
0.8081374657 3
 
0.5%
Other values (460) 532
93.5%
ValueCountFrequency (%)
0 1
0.2%
0.07942409403 1
0.2%
0.109534517 1
0.2%
0.1339794184 1
0.2%
0.1400739966 1
0.2%
0.1750404407 1
0.2%
0.1838656447 1
0.2%
0.1921657965 1
0.2%
0.1945522593 1
0.2%
0.1953469742 1
0.2%
ValueCountFrequency (%)
1 5
0.9%
0.9980057691 1
 
0.2%
0.9887760546 1
 
0.2%
0.9856900869 1
 
0.2%
0.9524752628 1
 
0.2%
0.947235402 1
 
0.2%
0.9388229415 1
 
0.2%
0.9335470855 1
 
0.2%
0.9165697522 1
 
0.2%
0.9144373471 1
 
0.2%

mean compactness
Real number (ℝ)

Distinct522
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60442071
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:05.784618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27161464
Q10.4551959
median0.60127279
Q30.74745163
95-th percentile0.9578989
Maximum1
Range1
Interquartile range (IQR)0.29225573

Descriptive statistics

Standard deviation0.20300369
Coefficient of variation (CV)0.33586489
Kurtosis-0.57914956
Mean0.60442071
Median Absolute Deviation (MAD)0.14617884
Skewness-0.010514498
Sum343.91538
Variance0.0412105
MonotonicityNot monotonic
2023-05-03T20:13:06.080051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16
 
2.8%
0.6919562699 3
 
0.5%
0.7135590945 3
 
0.5%
0.6782854123 2
 
0.4%
0.2492443153 2
 
0.4%
0.6805909299 2
 
0.4%
0.7424136964 2
 
0.4%
0.8136492199 2
 
0.4%
0.6122777501 2
 
0.4%
0.4096736326 2
 
0.4%
Other values (512) 533
93.7%
ValueCountFrequency (%)
0 1
0.2%
0.06756960753 1
0.2%
0.1119343809 1
0.2%
0.1153547652 1
0.2%
0.1714529864 1
0.2%
0.1827232512 1
0.2%
0.2031827962 1
0.2%
0.2037341054 1
0.2%
0.2098600851 1
0.2%
0.2164335514 1
0.2%
ValueCountFrequency (%)
1 16
2.8%
0.9995531141 1
 
0.2%
0.9979248485 1
 
0.2%
0.9903274266 1
 
0.2%
0.989706548 1
 
0.2%
0.9890849299 1
 
0.2%
0.9874236516 1
 
0.2%
0.9830374474 1
 
0.2%
0.9800926647 1
 
0.2%
0.9724429594 1
 
0.2%

mean symmetry
Real number (ℝ)

Distinct419
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59615764
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:06.364958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30147395
Q10.47050452
median0.59816615
Q30.70909704
95-th percentile0.91685843
Maximum1
Range1
Interquartile range (IQR)0.23859252

Descriptive statistics

Standard deviation0.18074584
Coefficient of variation (CV)0.30318464
Kurtosis-0.024405332
Mean0.59615764
Median Absolute Deviation (MAD)0.11761278
Skewness-2.6869591 × 10-5
Sum339.2137
Variance0.032669058
MonotonicityNot monotonic
2023-05-03T20:13:06.649651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
2.5%
0.5819105569 4
 
0.7%
0.5421798148 4
 
0.7%
0.4564589315 4
 
0.7%
0.5443790667 4
 
0.7%
0.667202324 4
 
0.7%
0.3879617192 3
 
0.5%
0.3796561239 3
 
0.5%
0.3467375966 3
 
0.5%
0.5465745427 3
 
0.5%
Other values (409) 523
91.9%
ValueCountFrequency (%)
0 1
0.2%
0.06030312895 1
0.2%
0.09827892258 1
0.2%
0.1106895117 1
0.2%
0.1158250488 1
0.2%
0.1700063604 1
0.2%
0.2000987854 1
0.2%
0.2029735509 1
0.2%
0.2304333287 1
0.2%
0.2323054556 1
0.2%
ValueCountFrequency (%)
1 14
2.5%
0.9974301662 1
 
0.2%
0.9766845407 1
 
0.2%
0.9682916737 1
 
0.2%
0.965657629 1
 
0.2%
0.9651301719 2
 
0.4%
0.9640746082 1
 
0.2%
0.9582534669 1
 
0.2%
0.9550671552 1
 
0.2%
0.9534710331 1
 
0.2%

mean fractal dimension
Real number (ℝ)

Distinct485
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56749164
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:06.938782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23028553
Q10.40919375
median0.5603462
Q30.70919159
95-th percentile0.94987744
Maximum1
Range1
Interquartile range (IQR)0.29999785

Descriptive statistics

Standard deviation0.21227425
Coefficient of variation (CV)0.37405705
Kurtosis-0.40341121
Mean0.56749164
Median Absolute Deviation (MAD)0.14972568
Skewness0.028092927
Sum322.90274
Variance0.045060356
MonotonicityNot monotonic
2023-05-03T20:13:07.213281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
2.6%
0.4687174461 3
 
0.5%
0.545473062 3
 
0.5%
0.3636789957 3
 
0.5%
0.4663012198 3
 
0.5%
0.7573575854 3
 
0.5%
0.4936767979 2
 
0.4%
0.5513105404 2
 
0.4%
0.4586040688 2
 
0.4%
0.5079934375 2
 
0.4%
Other values (475) 531
93.3%
ValueCountFrequency (%)
0 1
0.2%
0.0179517444 1
0.2%
0.018587675 1
0.2%
0.0306030015 1
0.2%
0.03687584275 1
0.2%
0.06284437334 1
0.2%
0.1106761318 1
0.2%
0.1112608917 1
0.2%
0.1159275482 1
0.2%
0.1378200126 1
0.2%
ValueCountFrequency (%)
1 15
2.6%
0.9992814394 1
 
0.2%
0.993495757 1
 
0.2%
0.9896626313 1
 
0.2%
0.9863539522 1
 
0.2%
0.9861695062 1
 
0.2%
0.9830237114 1
 
0.2%
0.9806050204 1
 
0.2%
0.9772371971 1
 
0.2%
0.9668089418 1
 
0.2%

radius error
Real number (ℝ)

Distinct503
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58809142
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:07.513501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21206866
Q10.41373313
median0.58054995
Q30.76125893
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3475258

Descriptive statistics

Standard deviation0.23877834
Coefficient of variation (CV)0.40602248
Kurtosis-0.69822528
Mean0.58809142
Median Absolute Deviation (MAD)0.17221555
Skewness0.026902359
Sum334.62402
Variance0.057015094
MonotonicityNot monotonic
2023-05-03T20:13:07.804599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38
 
6.7%
0.3860325492 3
 
0.5%
0.5191427827 3
 
0.5%
0.5524590094 2
 
0.4%
0.4668151391 2
 
0.4%
0.4753984699 2
 
0.4%
0.3942981208 2
 
0.4%
0.222162885 2
 
0.4%
0.3520703813 2
 
0.4%
0.4367907279 2
 
0.4%
Other values (493) 511
89.8%
ValueCountFrequency (%)
0 1
0.2%
0.01560834437 1
0.2%
0.0203545837 1
0.2%
0.02713119113 1
0.2%
0.03737907036 1
0.2%
0.04141945893 1
0.2%
0.04392799791 1
0.2%
0.04940221972 1
0.2%
0.07682945837 1
0.2%
0.09293668495 1
0.2%
ValueCountFrequency (%)
1 38
6.7%
0.9971946089 1
 
0.2%
0.9941530867 1
 
0.2%
0.9935413264 1
 
0.2%
0.9930227763 1
 
0.2%
0.992975594 1
 
0.2%
0.9916043032 1
 
0.2%
0.9886529234 1
 
0.2%
0.9860602605 1
 
0.2%
0.9846108816 1
 
0.2%

texture error
Real number (ℝ)

Distinct500
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55312914
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:08.084521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18514271
Q10.39867138
median0.5474198
Q30.70441763
95-th percentile0.94158878
Maximum1
Range1
Interquartile range (IQR)0.30574625

Descriptive statistics

Standard deviation0.22015874
Coefficient of variation (CV)0.39802412
Kurtosis-0.43001112
Mean0.55312914
Median Absolute Deviation (MAD)0.15329554
Skewness-0.010358857
Sum314.73048
Variance0.048469872
MonotonicityNot monotonic
2023-05-03T20:13:08.368293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
3.5%
0.620639758 3
 
0.5%
0.6552463115 3
 
0.5%
0.4121157234 3
 
0.5%
0.5674444498 3
 
0.5%
0.4497765188 2
 
0.4%
0.5166824294 2
 
0.4%
0.5232496663 2
 
0.4%
0.5900572933 2
 
0.4%
0.6519476763 2
 
0.4%
Other values (490) 527
92.6%
ValueCountFrequency (%)
0 1
0.2%
0.002321154089 1
0.2%
0.003173781792 1
0.2%
0.03196268832 1
0.2%
0.04450638823 1
0.2%
0.05378311583 1
0.2%
0.06050173419 1
0.2%
0.08291607458 1
0.2%
0.08312621481 1
0.2%
0.09001635705 1
0.2%
ValueCountFrequency (%)
1 20
3.5%
0.9979366347 1
 
0.2%
0.9763296166 1
 
0.2%
0.9716164874 1
 
0.2%
0.963428314 1
 
0.2%
0.9610339883 1
 
0.2%
0.955953611 1
 
0.2%
0.9548795771 1
 
0.2%
0.9489441928 1
 
0.2%
0.9437793051 1
 
0.2%

smoothness error
Real number (ℝ)

Distinct520
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66496928
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:08.683617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.38259346
Q10.55131941
median0.65692666
Q30.77974254
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.22842312

Descriptive statistics

Standard deviation0.17337561
Coefficient of variation (CV)0.26072725
Kurtosis-0.20826315
Mean0.66496928
Median Absolute Deviation (MAD)0.11653966
Skewness-0.00019132319
Sum378.36752
Variance0.030059102
MonotonicityNot monotonic
2023-05-03T20:13:08.967210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30
 
5.3%
0.6584198291 2
 
0.4%
0.9085323483 2
 
0.4%
0.6163012244 2
 
0.4%
0.730693457 2
 
0.4%
0.7581043404 2
 
0.4%
0.7445180283 2
 
0.4%
0.7391270647 2
 
0.4%
0.665820297 2
 
0.4%
0.5636781146 2
 
0.4%
Other values (510) 521
91.6%
ValueCountFrequency (%)
0 1
0.2%
0.2204306897 1
0.2%
0.2493196329 1
0.2%
0.2514340249 1
0.2%
0.2563333064 1
0.2%
0.2599766985 1
0.2%
0.3017555222 1
0.2%
0.3065058146 1
0.2%
0.3183429899 1
0.2%
0.3214123191 1
0.2%
ValueCountFrequency (%)
1 30
5.3%
0.9926830687 1
 
0.2%
0.9898327461 1
 
0.2%
0.9811846639 1
 
0.2%
0.9770142634 1
 
0.2%
0.9719642125 1
 
0.2%
0.9706938241 1
 
0.2%
0.9698451318 1
 
0.2%
0.9595487638 1
 
0.2%
0.9573771873 1
 
0.2%

compactness error
Real number (ℝ)

Distinct514
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62749857
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:09.247204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33164496
Q10.48060123
median0.62040754
Q30.77326135
95-th percentile0.99440258
Maximum1
Range1
Interquartile range (IQR)0.29266012

Descriptive statistics

Standard deviation0.20052941
Coefficient of variation (CV)0.31956951
Kurtosis-0.51223659
Mean0.62749857
Median Absolute Deviation (MAD)0.14841938
Skewness-0.012798768
Sum357.04669
Variance0.040212044
MonotonicityNot monotonic
2023-05-03T20:13:09.542372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28
 
4.9%
0.6598964665 3
 
0.5%
0.429560574 3
 
0.5%
0.5817932759 3
 
0.5%
0.551534128 2
 
0.4%
0.5232434051 2
 
0.4%
0.7525626066 2
 
0.4%
0.5929835178 2
 
0.4%
0.7662700549 2
 
0.4%
0.4552769281 2
 
0.4%
Other values (504) 520
91.4%
ValueCountFrequency (%)
0 1
0.2%
0.07235336532 1
0.2%
0.1258508357 1
0.2%
0.1283632722 1
0.2%
0.1859805342 1
0.2%
0.1878690628 1
0.2%
0.1888942746 1
0.2%
0.1985215273 1
0.2%
0.1994019231 1
0.2%
0.2011005506 1
0.2%
ValueCountFrequency (%)
1 28
4.9%
0.9947187096 1
 
0.2%
0.9939283754 1
 
0.2%
0.9905681159 1
 
0.2%
0.9897084691 1
 
0.2%
0.9869333366 1
 
0.2%
0.9855698961 1
 
0.2%
0.9808881901 1
 
0.2%
0.9716214481 1
 
0.2%
0.9666500857 1
 
0.2%

concave points error
Real number (ℝ)

Distinct488
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59403482
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:09.914046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29641902
Q10.4467703
median0.59236931
Q30.72518487
95-th percentile0.94547531
Maximum1
Range1
Interquartile range (IQR)0.27841457

Descriptive statistics

Standard deviation0.19501066
Coefficient of variation (CV)0.32828153
Kurtosis-0.2455679
Mean0.59403482
Median Absolute Deviation (MAD)0.139762
Skewness-0.010334212
Sum338.00581
Variance0.038029158
MonotonicityNot monotonic
2023-05-03T20:13:10.380968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
3.3%
0.4467703014 14
 
2.5%
0.6206775695 3
 
0.5%
0.5989897146 3
 
0.5%
0.7340132277 3
 
0.5%
0.5204110671 2
 
0.4%
0.632893228 2
 
0.4%
0.3519881457 2
 
0.4%
0.6569823714 2
 
0.4%
0.6270005488 2
 
0.4%
Other values (478) 517
90.9%
ValueCountFrequency (%)
0 1
0.2%
0.06689796406 1
0.2%
0.06895935346 1
0.2%
0.1243001134 1
0.2%
0.1259873475 1
0.2%
0.1438225761 1
0.2%
0.1547804861 1
0.2%
0.1631115669 1
0.2%
0.16824834 1
0.2%
0.1708383803 1
0.2%
ValueCountFrequency (%)
1 19
3.3%
0.9989613896 1
 
0.2%
0.98871722 1
 
0.2%
0.9854189319 1
 
0.2%
0.9843163501 1
 
0.2%
0.9829911588 1
 
0.2%
0.9702861326 1
 
0.2%
0.950706594 1
 
0.2%
0.9500143561 1
 
0.2%
0.9470072867 1
 
0.2%

symmetry error
Real number (ℝ)

Distinct472
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63187306
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:10.938529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31404307
Q10.49300782
median0.63085834
Q30.76789757
95-th percentile0.98604406
Maximum1
Range1
Interquartile range (IQR)0.27488975

Descriptive statistics

Standard deviation0.19514411
Coefficient of variation (CV)0.30883435
Kurtosis-0.50144787
Mean0.63187306
Median Absolute Deviation (MAD)0.13728784
Skewness0.018853604
Sum359.53577
Variance0.038081223
MonotonicityNot monotonic
2023-05-03T20:13:11.507611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
 
4.7%
0.41007791 4
 
0.7%
0.4646263141 3
 
0.5%
0.6298493032 3
 
0.5%
0.6345397999 3
 
0.5%
0.5018353071 3
 
0.5%
0.6853492919 3
 
0.5%
0.548189832 3
 
0.5%
0.6388538948 3
 
0.5%
0.6476894429 3
 
0.5%
Other values (462) 514
90.3%
ValueCountFrequency (%)
0 1
0.2%
0.155018276 1
0.2%
0.1877526155 1
0.2%
0.2018597134 1
0.2%
0.213918577 1
0.2%
0.2322691624 1
0.2%
0.2329911083 1
0.2%
0.2344322707 1
0.2%
0.238019347 1
0.2%
0.2401608175 2
0.4%
ValueCountFrequency (%)
1 27
4.7%
0.992885203 1
 
0.2%
0.9868047524 1
 
0.2%
0.9849030113 1
 
0.2%
0.9826969437 1
 
0.2%
0.9809229434 1
 
0.2%
0.9763019866 1
 
0.2%
0.9740460467 1
 
0.2%
0.9737442588 1
 
0.2%
0.9708654903 1
 
0.2%

worst symmetry
Real number (ℝ)

Distinct478
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59118218
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T20:13:11.987864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30206184
Q10.46611534
median0.5878361
Q30.71041243
95-th percentile0.96666463
Maximum1
Range1
Interquartile range (IQR)0.24429709

Descriptive statistics

Standard deviation0.19075683
Coefficient of variation (CV)0.32267013
Kurtosis0.18499837
Mean0.59118218
Median Absolute Deviation (MAD)0.1225299
Skewness0.001006939
Sum336.38266
Variance0.036388169
MonotonicityNot monotonic
2023-05-03T20:13:12.491964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
4.0%
0.4160688139 3
 
0.5%
0.6409675599 3
 
0.5%
0.3475638531 3
 
0.5%
0.7159310328 3
 
0.5%
0.6874018226 3
 
0.5%
0.4101297193 3
 
0.5%
0.722389386 2
 
0.4%
0.669326108 2
 
0.4%
0.5240335115 2
 
0.4%
Other values (468) 522
91.7%
ValueCountFrequency (%)
0 1
0.2%
0.0006203088567 1
0.2%
0.02332185482 1
0.2%
0.05029732153 1
0.2%
0.0526625634 1
0.2%
0.08752914879 1
0.2%
0.1273782435 2
0.4%
0.142697085 1
0.2%
0.1684630488 1
0.2%
0.1847834301 1
0.2%
ValueCountFrequency (%)
1 23
4.0%
0.9904745798 1
 
0.2%
0.9838242083 1
 
0.2%
0.9761031343 1
 
0.2%
0.9737767733 1
 
0.2%
0.973259171 1
 
0.2%
0.9678103749 1
 
0.2%
0.964946007 1
 
0.2%
0.9623358888 1
 
0.2%
0.9576228315 1
 
0.2%

Interactions

2023-05-03T20:13:00.161835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:16.398448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:19.372046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:22.369345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:25.638843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:30.052395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:32.988743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:36.014670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:38.949127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:43.082956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:46.626211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:52.537938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:55.918309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:00.383294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:16.626809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:19.597258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:22.586631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:26.013923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:30.280836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:33.218878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:36.245917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:39.159922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:43.476811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:46.848127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:52.755270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:56.271676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:00.613401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:16.849572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:19.842412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:22.818857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:26.359963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:30.495496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:33.452242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:36.474489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:39.366674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:43.817006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:47.061768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:52.995415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:56.576780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:00.840706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:17.067184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:20.074761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:23.043830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:26.748666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:30.704018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:33.676382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:36.709703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:39.623029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:44.186239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:47.267571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:53.221775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:56.879001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:01.063800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:17.300371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:20.303406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:23.275197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:27.080716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:30.925696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:33.887090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:36.940968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:39.891809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:44.567175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:47.504163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:53.453952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:57.219837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:01.291279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:17.537496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:20.530016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:23.501873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:27.363813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:31.136388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:34.138396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:37.167945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:40.254505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:44.821779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:47.723799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:53.687580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:57.588967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:01.541121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:17.770912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:20.778820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:23.728912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:27.727470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:31.377460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:34.404723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:37.409207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:40.558334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:45.040740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:47.966873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:53.927892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:57.984945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:01.758685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:17.989020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:21.020070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:23.972901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:28.115140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:31.588493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:34.634974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:37.624573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:40.913888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:45.259309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:48.188091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:54.160033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:58.306367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:01.982189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:18.207411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:21.237518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:24.198593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:28.441998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:31.827068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:34.860358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:37.842644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:41.295657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:45.492998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:48.417929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:54.391604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:58.700803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:02.193066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:18.423182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:21.453877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:24.429451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:28.739738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:32.070356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:35.081537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:38.058077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:41.661364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:45.740199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:48.635952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:54.637605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:59.082189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:02.422766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:18.671290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:21.679753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:24.682788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:29.129483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:32.314619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:35.325854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:38.270936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:42.023803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:45.953172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:48.882469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:54.921581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:59.458532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:02.640529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:18.912145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:21.916064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:24.957202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:29.523696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:32.530380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:35.559769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:38.510513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:42.331298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:46.169613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:49.109432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:55.255077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:59.726913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:13:02.864714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:19.152390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:22.130532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:25.255169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:29.834791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:32.757439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:35.788205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:38.731902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:42.693462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:46.381861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:52.312770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:55.621545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T20:12:59.944171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-03T20:13:12.959620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
mean radius1.0000.3410.1480.4970.120-0.3500.550-0.145-0.3270.2650.394-0.2420.175
mean texture0.3411.0000.0250.2660.110-0.0590.3640.4500.0380.2640.2480.0080.121
mean smoothness0.1480.0251.0000.6790.5420.5880.3330.0920.3390.3920.4390.1500.394
mean compactness0.4970.2660.6791.0000.5520.4990.5070.0480.1270.8180.7250.0980.450
mean symmetry0.1200.1100.5420.5521.0000.4280.3380.1390.2060.4360.3930.3830.710
mean fractal dimension-0.350-0.0590.5880.4990.4281.0000.0020.1570.4010.4810.2990.3140.296
radius error0.5500.3640.3330.5070.3380.0021.0000.3100.2230.4280.6130.2410.147
texture error-0.1450.4500.0920.0480.1390.1570.3101.0000.4440.2300.2850.389-0.120
smoothness error-0.3270.0380.3390.1270.2060.4010.2230.4441.0000.2840.3680.473-0.067
compactness error0.2650.2640.3920.8180.4360.4810.4280.2300.2841.0000.7590.2710.266
concave points error0.3940.2480.4390.7250.3930.2990.6130.2850.3680.7591.0000.2900.129
symmetry error-0.2420.0080.1500.0980.3830.3140.2410.3890.4730.2710.2901.0000.281
worst symmetry0.1750.1210.3940.4500.7100.2960.147-0.120-0.0670.2660.1290.2811.000

Missing values

2023-05-03T20:13:03.219597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-03T20:13:03.682459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
00.8505900.0536820.8372021.0000000.9766850.9992811.0000000.4409150.6584200.9176160.7610220.9058581.000000
10.9533190.5085150.4208240.5332590.6121360.3636790.8166500.3341770.5567220.4806010.6822250.4330870.561413
20.9202400.6689990.7370050.8373440.7792710.5026060.9485550.3692040.6384930.8459820.8899450.7428280.843557
30.4711320.6310621.0000001.0000001.0000001.0000000.7764150.5702560.8360341.0000000.8404731.0000001.000000
40.9429950.3222250.6250230.7554060.6100500.4565670.9547670.3655960.9530060.6806550.8452840.5898300.408001
50.5471150.4001270.9388230.8647470.7901990.9506690.5956100.4322170.7388590.7836210.6093590.7198770.946551
60.8617800.6131640.5532690.6701280.5995700.3970480.7301330.3603410.4607380.4974000.5708450.4229850.672022
70.6297360.6508500.8427420.8500090.8544580.9176970.8470740.6662610.8188900.7498960.7178440.4794720.715931
80.5844470.6931570.9335470.9225960.9400920.9045380.5529380.4939430.6030810.7993500.6423510.7137401.000000
90.5478130.7824940.8394201.0000000.7552691.0000000.5387990.7506420.7140961.0000000.7126880.6017621.000000
mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
5590.4781180.7782330.5270050.6423070.2773340.6967490.4278041.0000000.7830670.7446190.6569820.4803850.295006
5600.6503670.8967090.6124460.6840290.4052290.5664320.6364430.7112690.7215630.7086140.7726540.6956930.358315
5610.4537530.9717280.2720350.2210070.0000000.2857160.5652061.0000000.7444520.3655180.4467700.6682990.000620
5620.7167241.0000000.6800450.9578990.8147490.8512430.4717140.5927740.4955950.9132040.7673100.7120520.973777
5630.9659730.8223670.7405090.9897070.8271440.7833501.0000000.5064460.6584200.8716801.0000000.6889220.625991
5640.9884210.7167940.7533000.6964290.5509540.3435171.0000000.6154210.8978800.7341920.9829910.2740400.270169
5650.9370120.9344960.5937320.6476730.5697610.3008640.9591401.0000000.6063970.6755380.7878600.6391840.493282
5660.7870160.9287370.4181800.6431360.4477990.3550270.7397900.5312420.6179190.8212210.7519360.3963110.343957
5670.9544150.9704190.8305341.0000000.9651300.8183500.9377880.7492100.6679811.0000000.7837990.7618890.973259
5680.1069850.8016590.0000000.2985730.4454270.4569750.6629060.6865900.7169010.1859810.4467700.8426150.605470